CausalDS: New Benchmark Tests Causal Reasoning in Data-Science Agents

Jul 10, 2026

Researchers introduced CausalDS, a benchmark for evaluating causal reasoning in LLM-based data-science agents. It generates tasks from synthetic structural causal models, covering all three rungs of Pearl's causal hierarchy, and includes data science coding components with imperfect observations. The benchmark also scores abstention when questions have no warranted answer.

Why it matters: CausalDS fills a gap by jointly evaluating symbolic causal reasoning, data science skills, tool use, and uncertainty quantification in a single benchmark.

Full story at: arXiv AI/ML ↗